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通过深度神经网络和树搜索掌握围棋游戏
David Silver1, Aja Huang1, Chris J Maddison1
1Google DeepMind, 5 New Street Square, London EC4A 3TW, UK.
Nature
|January 29, 2016
概括
人工智能 (AI) 现在在使用深度神经网络的围棋游戏中表现出色. AlphaGo结合了监督和强化学习,与其他程序相比,获得了99.8%的胜利率.
科学领域:
- 人工智能
- 计算机科学
- 游戏理论
背景情况:
- 围棋游戏对人工智能提出了重大挑战,因为它拥有庞大的搜索空间和复杂的动作评估.
- 传统的人工智能方法难以应对围棋的复杂性,使得专家级别的游戏成为长期目标.
研究的目的:
- 用深度神经网络开发一种新的围棋人工智能方法.
- 通过先进的机器学习技术实现人类专业水平的表现.
主要方法:
- 引入用于评价董事会职位的"价值网络"和用于选择职位的"政策网络".
- 通过人类专家游戏的监督学习和自动游戏的强化学习来训练深度神经网络.
- 开发一个新的搜索算法,将蒙特卡洛模拟与价值和政策网络整合起来.
主要成果:
- 只有神经网络,而没有镜头搜索,可以实现与最先进的蒙特卡洛树搜索程序相提并论的性能.
- 通过使用新的搜索算法,AlphaGo的获胜率高达99.8%.
- 阿尔法戈以5比0击败了人类的欧洲围棋冠军, 标志着AI能力的重要里程碑.
结论:
- 通过新的学习方法进行训练, 可以在复杂的战略游戏中实现超人性能.
- 这项研究克服了游戏人工智能的长期挑战,展示了AI在战略领域的新范式.
- 阿尔法戈的成功标志着一个重大进步,实现了人工智能研究的长期目标,并可能影响其他复杂的决策领域.
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